As an alternative framework to generative adversarial networks (GANs) [1]}, score-based generative models (SGMs) have recently demonstrated excellent abilities in data synthesis (e.g., high resolution images) with easier optimization [2]} and richer generative diversity [3]}.
Starting from a sample initialized with a Gaussian distribution, a SGM produces a target sample by recurrently adding a learned gradient and a Gaussian noise to this sample, namely the diffusion process.
